MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution

Główne pojęcia

MAGIS proposes a novel LLM-based Multi-Agent framework for GitHub Issue resolution, significantly outperforming popular LLMs.

Streszczenie

The content introduces MAGIS, a framework for resolving GitHub issues using Large Language Models (LLMs). It addresses challenges faced by LLMs in code change tasks at the repository level. The framework consists of four agents: Manager, Repository Custodian, Developer, and Quality Assurance Engineer. Experiments show MAGIS outperforms GPT-4, achieving a resolved ratio of 13.94%. Factors like line location and code complexity impact issue resolution rates.
Abstract

LLMs face challenges in resolving GitHub issues at the repository level.
MAGIS proposes a Multi-Agent framework leveraging LLMs for issue resolution.
Experiments show MAGIS significantly outperforms popular LLMs.
Introduction

Software evolution requires addressing emergent bugs and adapting to new requirements.
GitHub issues signify the need for software evolution.
LLMs excel in code generation but face challenges in advanced tasks like GitHub issue resolution.
Methodology

MAGIS framework involves four agents collaborating in planning and coding.
Planning phase involves locating code files and team building.
Coding phase includes developers generating code changes and QA engineers reviewing them.
Experiments and Analysis

MAGIS significantly outperforms GPT-4 and Claude-2 in resolving GitHub issues.
Planning process effectiveness demonstrated through repository custodian and project manager agent analysis.
Coding process effectiveness analyzed through line location overlap and complexity indices correlation.

Statystyki

MAGIS can resolve 13.94% of GitHub issues, outperforming GPT-4 significantly.

Cytaty

"LLMs excel in generating function-level code but face challenges in code change tasks."
"MAGIS achieves an eight-fold increase in resolved ratio over GPT-4."

Kluczowe wnioski z

MAGIS

by Wei Tao,Yuch... o arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17927.pdf

Głębsze pytania

How can MAGIS be adapted for other software development tasks

MAGIS can be adapted for other software development tasks by customizing the roles and workflows of the agents to suit the specific requirements of different tasks. For instance, in tasks that involve code refactoring, the Developer agent can be trained to focus on restructuring code while maintaining functionality. The Manager agent can prioritize tasks related to code optimization and performance enhancement. The QA Engineer agent can ensure that the refactored code meets quality standards and does not introduce new bugs. By tailoring the roles and responsibilities of the agents, MAGIS can effectively address a wide range of software development tasks beyond GitHub issue resolution.

What are the potential limitations of relying solely on LLMs for issue resolution

Relying solely on LLMs for issue resolution may have several limitations. One potential limitation is the inability of LLMs to fully understand the context and nuances of complex software development tasks. LLMs may struggle with interpreting code changes in the broader context of the software repository, leading to suboptimal solutions. Additionally, LLMs may face challenges in handling intricate code logic and dependencies, which are crucial for successful issue resolution. Moreover, LLMs may lack the ability to incorporate domain-specific knowledge or best practices, which could impact the quality and effectiveness of the generated code changes.

How might the collaboration among agents in MAGIS inspire new approaches to problem-solving in software development

The collaboration among agents in MAGIS can inspire new approaches to problem-solving in software development by promoting teamwork, specialization, and quality assurance. By leveraging the strengths of different agents, such as the Manager for task allocation, the Repository Custodian for file location, the Developer for code generation, and the QA Engineer for quality assurance, MAGIS fosters a collaborative environment where each agent contributes unique expertise to the issue resolution process. This collaborative approach can lead to more efficient problem-solving, improved code quality, and enhanced software development practices. The success of MAGIS highlights the importance of teamwork and coordination in addressing complex software development challenges.